Errors that result from failures of perception occur with all medical images including sophisticated tomographic technology such as CT, MRI, PET/CT and tomosynthesis, and these errors have significant health consequences. Basic scientific research on image perception strives to uncover the causes of these errors and provide remedies. In the past, perception research has relied on a very limited range of simulated abnormalities from phantoms or on diverse "found" abnormalities in clinical studies. Compiling samples of proven normal and abnormal imaging examinations by that approach is extremely time consuming and limits perception and image processing research. The broad, long-term objective of our application is to advance perception research in tomographic imaging modalities. Our goals are (1) to develop methods for altering tomographic clinical studies by removing abnormalities, storing them, and reintroducing them into normal image regions and (2) to psychophysically evaluate the success of those methods. These goals will be accomplished through 4 specific aims: Our first specific aim develops software to remove localized three-dimensional abnormalities from medical tomographic images without leaving any trace of image manipulation. Our second specific aim develops software to capture abnormal areas from specific organs and specific modalities and organize the volumetric collections of these abnormal areas into library files. This will yield organ-specific and modality-specific abnormality libraries for use in perception research, computer-aided diagnosis development and observer modeling. Our third specific aim develops interactive software to insert abnormalities from the libraries into medical tomographic images without introducing artifacts that would identify them as artificially placed. As part of aims 1 &3, evaluation tools will be developed and provided to verify that no visual cues are created from the manipulations. The fourth specific aim provides for dissemination of the software and libraries to the medical image perception, computer-aided diagnosis, and observer modeling communities. By making these abnormality manipulation tools available, this project will enable more and better research to reduce or eliminate diagnostic error, thereby improving public health.